With the advent of computer-based communications, the concept of text can mean many different things, such as online surveys, feedback forms, chat dialog, social media interactions and conversations, and so forth. These types of unstructured computer text are present across all business domains in a variety of forms. Manual interpretation of such unstructured text to glean useful, business-actionable information is inefficient and time-consuming.
Recently, computing systems and related technical resources have been applied to interpret the unstructured computer text in order to understand underlying context, themes and sentiment the text. In one example, data mining and text processing applications have been used to, e.g., extract sentiment from unstructured computing text in order to determine items such as customeremotions and preferences. However, such computerized techniques are generally inaccurate because they are limited to a high-level review of unstructured text. For example, computer platforms previously existed that automated the complaint registration process—such as the systems and methods for automating slamming and cramming complaints described in U.S. Pat. No. 6,853,722 to Joseph et al. where the system identifies the customer calling in using, e.g., a calling telephone number and then analyzes recent telephone activity to extrapolate the type of complaint that the customer might want to register. There, the system looks at only two basic types of complaints, a slam or a cram—without using advanced computing techniques to analyze the underlying computer text semantically to classify more specific types of complaints.